7,684 research outputs found
GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos
The interest in video anomaly detection systems that can detect different types of anomalies,
such as violent behaviours in surveillance videos, has gained traction in recent years. The current
approaches employ deep learning to perform anomaly detection in videos, but this approach has
multiple problems. For example, deep learning in general has issues with noise, concept drift,
explainability, and training data volumes. Additionally, anomaly detection in itself is a complex
task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly
detection using deep learning is therefore mainly constrained to generative models such as generative
adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer
from general deep learning issues and are hard to properly train. In this paper, we explore the
capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection
in videos, as it has favorable properties such as noise tolerance and online learning which combats
concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM
architecture specifically for anomaly detection in complex videos such as surveillance footage. We
have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation
results and online learning capabilities prove the great potential of using our system for real-time
unsupervised anomaly detection in complex videos
Practical Application of Machine Learning based Online Intrusion Detection to Internet of Things Networks
Internet of Things (IoT) devices participate in an open and distributed perception layer, with vulnerability to cyber attacks becoming a key concern for data privacy and service availability. The perception layer provides a unique challenge for intrusion detection where resources are constrained and networks are distributed. An additional challenge is that IoT networks are a
continuous non-stationary data stream that, due to their variable nature, are likely to experience concept drift. This research aimed to review the practical applications of online machine learning methods for IoT network intrusion detection, to answer the question if a resource efficient architecture can be provided? An online learning architecture is introduced, with related IDS approaches reviewed and evaluated. Online learning provides a potential memory and time efficient architecture that can adapt to concept drift and perform anomaly detection, providing solutions for the resource constrained and distributed IoT perception layer.
Future research should focus on addressing class imbalance in
the data streams to ensure that minority attack classes are not
missed
Evaluation methods and decision theory for classification of streaming data with temporal dependence
Predictive modeling on data streams plays an important role in modern data analysis, where data arrives continuously and needs to be mined in real time. In the stream setting the data distribution is often evolving over time, and models that update themselves during operation are becoming the state-of-the-art. This paper formalizes a learning and evaluation scheme of such predictive models. We theoretically analyze evaluation of classifiers on streaming data with temporal dependence. Our findings suggest that the commonly accepted data stream classification measures, such as classification accuracy and Kappa statistic, fail to diagnose cases of poor performance when temporal dependence is present, therefore they should not be used as sole performance indicators. Moreover, classification accuracy can be misleading if used as a proxy for evaluating change detectors with datasets that have temporal dependence. We formulate the decision theory for streaming data classification with temporal dependence and develop a new evaluation methodology for data stream classification that takes temporal dependence into account. We propose a combined measure for classification performance, that takes into account temporal dependence, and we recommend using it as the main performance measure in classification of streaming data
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data
Each year, thousands of software vulnerabilities are discovered and reported
to the public. Unpatched known vulnerabilities are a significant security risk.
It is imperative that software vendors quickly provide patches once
vulnerabilities are known and users quickly install those patches as soon as
they are available. However, most vulnerabilities are never actually exploited.
Since writing, testing, and installing software patches can involve
considerable resources, it would be desirable to prioritize the remediation of
vulnerabilities that are likely to be exploited. Several published research
studies have reported moderate success in applying machine learning techniques
to the task of predicting whether a vulnerability will be exploited. These
approaches typically use features derived from vulnerability databases (such as
the summary text describing the vulnerability) or social media posts that
mention the vulnerability by name. However, these prior studies share multiple
methodological shortcomings that inflate predictive power of these approaches.
We replicate key portions of the prior work, compare their approaches, and show
how selection of training and test data critically affect the estimated
performance of predictive models. The results of this study point to important
methodological considerations that should be taken into account so that results
reflect real-world utility
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